Virtual Vehicle Coordination for Vehicles as Ambient Sensing Platforms

With the increasing number of in-vehicle sensors and the adoption of various communication interfaces, vehicles have become an ambient sensing platform for many applications such as traffic and weather monitoring, urban surveillance, road safety, and driver behavior analysis. Furthermore, a large number of vehicles can be coordinated to sense large-scale information precisely and efficiently. Such coordination requires enormous communication, computing, and power resources. In this paper, we propose a virtual vehicle (VV) coordination approach based on the theory of group consensus to sense environment information efficiently. Specifically, we first propose a discovery algorithm to find the optimal VV groups. Then, based on multi-group consensus theory, we devise a coordination algorithm to control VV groups to achieve multi-group coordination by adjusting the communication relationships among VVs. Finally, the precise environment information is acquired through the coordination of multiple VV groups. Extensive simulations are provided to demonstrate the effectiveness of the proposed schemes.